DOCSLIB.ORG

Rdock Reference Guide Rdock Development Team August 27, 2015 Contents

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

rDock
Reference Guide

rDock Development Team
August 27, 2015

Contents

12345

  • Preface
  • 4

444
Acknowledgements Introduction Configuration Cavity mapping

5.1 Two sphere method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Reference ligand method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

6

68

  • 6
  • Scoring function reference
  • 11

6.1 Component Scoring Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
6.1.1 van der Waals potential . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 6.1.2 Empirical attractive and repulsive polar potentials . . . . . . . . . . . . . . . . . . 11 6.1.3 Solvation potential . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 6.1.4 Dihedral potential . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
6.2 Intermolecular scoring functions under evaluation . . . . . . . . . . . . . . . . . . . . . . . 13
6.2.1 Training sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 6.2.2 Scoring Functions Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 6.2.3 Scoring Functions Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
6.3 Code Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

  • 7
  • Docking protocol
  • 17

7.1 Protocol Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
7.1.1 Pose Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 7.1.2 Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 7.1.3 Monte Carlo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 7.1.4 Simplex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
7.2 Code Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 7.3 Standard rDock docking protocol (dock.prm) . . . . . . . . . . . . . . . . . . . . . . . . . 18

  • 8
  • System definition file reference
  • 22

8.1 Receptor definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 8.2 Ligand definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 8.3 Solvent definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 8.4 Cavity mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 8.5 Cavity restraint . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 8.6 Pharmacophore restraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 8.7 NMR restraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 8.8 Example system definition files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

  • 9
  • Molecular files and atoms typing
  • 30

9.1 Atomic properties. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 9.2 Difference between formal charge and distributed formal charge . . . . . . . . . . . . . . . 30 9.3 Parsing a MOL2 file . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 9.4 Parsing an SD file . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 9.5 Assigning distributed formal charges to the receptor . . . . . . . . . . . . . . . . . . . . . 31

  • 10 rDock file formats
  • 32

10.1 .prm file format . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 10.2 Water PDB file format . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 10.3 Pharmacophore restraints file format . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

2

  • 11 rDock programs
  • 35

11.1 Programs reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
11.1.1 rbdock . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 11.1.2 rbcavity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 11.1.3 rbcalcgrid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 11.1.4 make grid.csh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 11.1.5 rbmoegrid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 11.1.6 sdrmsd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 11.1.7 sdtether . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 11.1.8 sdfilter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 11.1.9 sdreport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 11.1.10sdsplit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 11.1.11sdsort . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 11.1.12sdmodify . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 11.1.13rbhtfinder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 11.1.14rblist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

  • 12 Common Use cases
  • 42

12.1 Standard docking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
12.1.1 Standard docking workflow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
12.2 Tethered scaffold docking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
12.2.1 Example ligand definition for tethered scaffold . . . . . . . . . . . . . . . . . . . . 43
12.3 Docking with pharmacophore restraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 12.4 Docking with explicit waters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

  • 13 Appendix
  • 45

3

1 Preface

It is intended to develop this document into a full reference guide for the rDock platform, describing the software tools, parameter files, scoring functions, and search engines. The reader is encouraged to crossreference the descriptions with the corresponding source code files to discover the finer implementation details.

2 Acknowledgements

Third-party source code. Two third-party C++ libraries are included within the rDock source code, to provide support for specific numerical calculations. The source code for each library can be distributed freely without licensing restrictions and we are grateful to the respective authors for their contributions.

• Nelder-Meads Simplex search, from Prof. Virginia Torczon’s group, College of William and Mary,
Department of Computer Science, VA. (http://www.cs.wm.edu/va/software/)

• Template Numerical Toolkit, from Roldan Pozo, Mathematical and Computational Sciences Division, National Institute of Standards and Technology (http://math.nist.gov/tnt/)

3 Introduction

The rDock platform is a suite of command-line tools for high-throughput docking and virtual screening. The programs and methods were developed and validated from 1998 to 2002 at RiboTargets (more recently, Vernalis) for proprietary use. The original program (RiboDock) was designed for high-throughput virtual screening of large ligand libraries against RNA targets, in particular the bacterial ribosome. Since 2002 the programs have been substantially rewritten and validated for docking of drug-like ligands to protein and RNA targets. A variety of experimental restraints can be incorporated into the docking calculation, in support of an integrated Structure-Based Drug Design process. In 2006, the software was licensed to the University of York for maintenance and distribution and, in 2012, Vernalis and the University of York agreed to release the program as Open Source software. rDock is licensed under GNU-LGPL version 3.0 with support from the University of Barcelona - rdock.sourceforge.net.

4 Configuration

Before launching rDock, make sure the following environment variables are defined. Precise details are likely to be site-specific.

• RBT ROOT environment variable: should be defined to point to the rDock installation directory.

• RBT HOME environment variable: is optional, but can be defined to to point to a user project directory containing rDock input files and customised data files.

• PATH environment variable: $RBT ROOT/bin should be added to the $PATH environment variable.

• LD LIBRARY PATH. $RBT ROOT/lib should be added to the $LD LIBRARY PATH environment variable.

Input file locations. The search path for the majority of input files for rDock is:
• Current working directory • $RBT HOME, if defined • The appropriate subdirectory of $RBT ROOT/data/. For example, the default location for scoring function files is $RBT ROOT/data/sf/.

4
The exception is that input ligand SD files are always specified as an absolute path. If you wish to customise a scoring function or docking protocol, it is sufficient to copy the relevant file to the current working directory or to $RBT HOME, and to modify the copied file.

Launching rDock. For small scale experimentation, the rDock executables can be launched directly from the command line. However, serious virtual screening campaigns will likely need access to a compute farm. In common with other docking tools, rDock uses the embarrassingly parallel approach to distributed computing. Large ligand libraries are split into smaller chunks, each of which is docked independently on a single machine. Docking jobs are controlled by a distributed resource manager (DRM) such as Condor or SGE.

5

5 Cavity mapping

Virtual screening is very rarely conducted against entire macromolecules. The usual practice is to dock small molecules in a much more confined region of interest. rDock makes a clear distinction between the region the ligand is allowed to explore (known here as the docking site), and the receptor atoms that need to be included in order to calculate the score correctly. The former is controlled by the cavity mapping algorithm, whilst the latter is scoring function dependent as it depends on the distance range of each component term (for example, vdW range >> polar range). For this reason, it is usual practice with rDock to prepare intact receptor files (rather than truncated spheres around the region of interest), and to allow each scoring function term to isolate the relevant receptor atoms within range of the docking site. rDock provides two methods for defining the docking site:

• Two sphere method • Reference ligand method

Note All the keywords found in capital letters in following cavity mapping methods explanation (e.g. RADIUS), make reference to the parameters defined in prm rDock configuration file. For more information, go to section 8.4 - Cavity mapping on page 25.

5.1 Two sphere method

The two sphere method aims to find cavities that are accessible to a small sphere (of typical atomic or solvent radius) but are inaccessible to a larger sphere. The larger sphere probe will eliminate flat and convex regions of the receptor surface, and also shallow cavities. The regions that remain and are accessible to the small sphere are likely to be the nice well defined cavities of interest for drug design purposes.
1. A grid is placed over the cavity mapping region, encompassing a sphere of radius=RADIUS, center=CENTER. Cavity mapping is restricted to this sphere. All cavities located will be wholly within this sphere. Any cavity that would otherwise protrude beyond the cavity mapping sphere will be truncated at the periphery of the sphere.

6
2. Grid points within the volume occupied by the receptor are excluded (coloured red). The radii of the receptor atoms are increased temporarily by VOL INCR in this step.

3. Probes of radii LARGE SPHERE are placed on each remaining unallocated grid point and checked for clashes with receptor excluded volume. To eliminate edge effects, the grid is extended beyond the cavity mapping region by the diameter of the large sphere (for this step only). This allows the large probe to be placed on grid points outside of the cavity mapping region, yet partially protrude into the cavity mapping region.

4. All grid points within the cavity mapping region that are accessible to the large probe are excluded
(coloured green).

7
5. Probes of radii SMALL SPHERE are placed on each remaining grid point and checked for clashes with receptor excluded volume (red) or large probe excluded volume (green)

6. All grid points that are accessible to the small probe are selected (yellow). 7. The final selection of cavity grid points is divided into distinct cavities (contiguous regions).
In this example only one distinct cavity is found. User-defined filters of MIN VOLUME and MAX CAVITIES are applied at this stage to select a subset of cavities if required. Note that the filters will accept or reject intact cavities only.

5.2 Reference ligand method

The reference ligand method provides a much easier option to define a docking volume of a given size around the binding mode of a known ligand, and is particularly appropriate for large scale automated validation experiments.

8
1. Reference coordinates are read from REF MOL. A grid is placed over the cavity mapping region, encompassing overlapping spheres of radius=RADIUS, centered on each atom in REF MOL. Grid points outside of the overlapping spheres are excluded (coloured green). Grid points within the volume occupied by the receptor are excluded (coloured red). The vdW radii of the receptor atoms are increased by VOL INCR in this step.

2. Probes of radii SMALL SPHERE are placed on each remaining grid point and checked for clashes with red or green regions.

3. All grid points that are accessible to the small probe are selected (yellow).

9
4. The final selection of cavity grid points is divided into distinct cavities (contiguous regions).
In this example only one distinct cavity is found. User-defined filters of MIN VOLUME and MAX CAVITIES are applied at this stage to select a subset of cavities if required. Note that the filters will accept or reject intact cavities only.

10

6 Scoring function reference

6.1 Component Scoring Functions

The rDock master scoring function (Stotal) is a weighted sum of intermolecular (Sinter), ligand intramolecular (Sintra), site intramolecular (Ssite), and external restraint terms (Srestraint) (Equation 1). Sinter is the main term of interest as it represents the protein-ligand (or RNA-ligand) interaction score (Equation 2). Sintra represents the relative energy of the ligand conformation (Equation 3). Similarly, Ssite represents the relative energy of the flexible regions of the active site (Equation 4). In the current implementation, the only flexible bonds in the active site are terminal OH and NH3+ bonds. Srestraint is a collection of non-physical restraint functions that can be used to bias the docking calculation in several useful ways (Equation 5).

Stotal = Sinter + Sintra + Ssite + Srestraint

(1)

inter vdw inter vdw

  • inter
  • inter

polar inter repul inter repul inter arom inter arom

Sinter

=+===

W

· S

+ W

polar

· S

+ W

· S

+ W

· S

+ Wsolv · Ssolv

+

Wrot · Nrot + Wconst

(2) (3) (4) (5)

intra vdw site

  • intra
  • intra
  • intra
  • intra

repul

  • intra
  • intra

dihedral intra dihedral

Sintra Ssite
Srestraint

WW

· S

+ W

· S

+ W

site

+ W

· S

+ W

site

+ W

· S

dihedral vdw site vdw polar site

  • polar
  • repul

  • site
  • site
  • site

· S

+ W

  • · S
  • · S
  • · S

  • vdw
  • polar
  • polar
  • repul
  • repul
  • dihedral

Wcavity · Scavity + Wtether · Stether + Wnmr · Snmr + Wph4 · Sph4

Sinter, Sintra, and Ssite are built from a common set of constituent potentials, which are described below. The main changes to the original RiboDock scoring function [ref] are:

i the replacement of the crude steric potentials (Slipo and Srep) with a true van der Waals potential,

SvdW

ii the introduction of two generalised terms for all short range attractive (Spolar) and repulsive (Srepul polar interactions
)iii the implementation of a fast weighed solvent accessible surface area (WSAS) solvation term iv the addition of explicit dihedral potentials

6.1.1 van der Waals potential

We have replaced the Slipo and Srep empirical potentials used in RiboDock with a true vdW potential similar to that used by GOLD [ref]. Atom types and vdW radii were taken from the Tripos 5.2 force field and are listed in the Appendix Section (page 45, table 13.1). Energy well depths are switchable between the original Tripos 5.2 values and those used by GOLD, which are calculated from the atomic polarisability and ionisation potentials of the atom types involved. Additional atom types were created for carbons with implicit hydrogens, as the Tripos force field uses an all-atom representation. vdW radii
˚for implicit hydrogen types are increased by 0.1 A for each implicit hydrogen, with well depths unchanged.

The functional form is switchable between a softer 4-8 and a harder 6-12 potential. A quadratic potential is used at close range to prevent excessive energy penalties for atomic clashes. The potential is truncated at longer range (1.5 · rmin, the sum of the vdW radii).
The quadratic potential is used at repulsive energies between ecutoff and e0, where ecutoff is defined as a multiple of the energy well depth (ecutoff = ECUT * emin), and e0 is the energy at zero separation, defined as a multiple of ecutoff (e0 = E0 * ecutoff ). ECUT can vary between 1 and 120 during the docking search (see below)[ref to ga section], whereas E0 is fixed at 1.5.

6.1.2 Empirical attractive and repulsive polar potentials

We continue to use an empirical Bohm-like potential to score hydrogen-bonding and other short-range polar interactions. The original RiboDock polar terms (SH−bond, SposC−acc, Sacc−acc, Sdon−don) are generalised and condensed into two scoring functions, Spolar and Srepul (Equations 6 and 7, also taking into account equations 8 to 13), which deal with attractive and repulsive interactions respectively. Six types of polar interaction centres are considered: hydrogen bond donors (DON), metal ions (M+), positively charged carbons (C+, as found at the centre of guanidinium, amidinium and imidazole groups), hydrogen

11 bond acceptors with pronounced lone pair directionality (ACC LP), acceptors with in-plane preference but limited lone-pair directionality (ACC PLANE), and all remaining acceptors (ACC). The ACC LP type is used for carboxylate oxygens and Osp2 atoms in RNA bases, with ACC PLANE used for other Osp2 acceptors. This distinction between acceptor types was not made in RiboDock, in which all acceptors were implicitly of type ACC.

Recommended publications
  • Evaluation of Protein-Ligand Docking Methods on Peptide-Ligand

    Evaluation of Protein-Ligand Docking Methods on Peptide-Ligand

    bioRxiv preprint doi: https://doi.org/10.1101/212514; this version posted November 1, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Evaluation of protein-ligand docking methods on peptide-ligand complexes for docking small ligands to peptides Sandeep Singh1#, Hemant Kumar Srivastava1#, Gaurav Kishor1#, Harinder Singh1, Piyush Agrawal1 and G.P.S. Raghava1,2* 1CSIR-Institute of Microbial Technology, Sector 39A, Chandigarh, India. 2Indraprastha Institute of Information Technology, Okhla Phase III, Delhi India #Authors Contributed Equally Emails of Authors: SS: [email protected] HKS: [email protected] GK: [email protected] HS: [email protected] PA: [email protected] * Corresponding author Professor of Center for Computation Biology, Indraprastha Institute of Information Technology (IIIT Delhi), Okhla Phase III, New Delhi-110020, India Phone: +91-172-26907444 Fax: +91-172-26907410 E-mail: [email protected] Running Title: Benchmarking of docking methods 1 bioRxiv preprint doi: https://doi.org/10.1101/212514; this version posted November 1, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. ABSTRACT In the past, many benchmarking studies have been performed on protein-protein and protein-ligand docking however there is no study on peptide-ligand docking.
  • Ftitr Tutirgr Held Thuraday

    Ftitr Tutirgr Held Thuraday

    San Jon, Cal. fhit 31nsr Pasffie Game Subs Rate, $1.00 Rally to be Per Quarter ftitr Tutirgr Held Thuraday 2 2 X! I . J1,1-1 11.1FURNiA, WED \ I --1).\1., (2LI OBER II, 19i; r_12 Attractive Program for NOTICE Concert Series Tickets Placed on In order to choose a yell leader Pre-game Rally Planned; for the coming year, an election Sale Today, Committee Announces; will be held in front of the Morris Dailey auditorium all dily today, Parade Will Climax Fun Wednesday, October II. Presi- Naom Blinder, Violinist, Here Nov. 7 dent Covello asks every tudent Wildest Event of Evening Will to cooperate by voting. The Car.- Probably Be Parade Thru Leads Rally Famous Violinist To Appear Concert Series Tickets Sale didate re Howard Burns and Enthusiasm Downtown San Jose 7 (,hairman Shown Jim Hamilton. At S. J. State Nov. 'psis By Students Will In Music Program Dud DeGroot and Team - - -- Speeches During Alice Dixon Heads Conimittee Give \ the initial concert in the IO33- +4 Rally In Auditorium E. Higgins Of Music Department _ _ Plans rie, San Jose State music 'rivers will Concert &ries iniee the opportunity to bear Naom di,. ,rantest pre -game rally in the Radio Pep Rally Blinder. famous violinist, N9V. 7. ot San Jose State will be held in The tickets tor 'la Blinder is now concert master of th, series go on sal,- today '. Morris Dailey auditorium thit ir. qa. -peed san Francisco Symphony orchestra, and The!. may also be ! mening at 7:30, according to At Station KQW porch:, .
  • What Is Discovery Studio?

    What Is Discovery Studio?

    Discovery Studio 2.0 Accelrys Life Science Tool 林進中 分子視算 What is Discovery Studio? • Discovery Studio is a complete modelling and simulations environment for Life Science researchers – Interactive, visual and integrated software – Consistent, contemporary user interface for added ease-of-use – Tools for visualisation, protein modeling, simulations, docking, pharmacophore analysis, QSAR and library design – Access computational servers and tools, share data, monitor jobs, and prepare and communicate their project progress – Windows and Linux clients and servers Accelrys Discovery Studio Application Discovery Studio Pipeline ISV Materials Discovery Accord WeWebbPPortort Pipeline ISV Materials Discovery Accord (web Studio Studio (web PilotPilot ClientClient Studio Studio ClientsClients access) (Pro or Lite ) (e.g., Client Client access) (Pro or Lite ) (e.g., Client Client Spotfire) Spotfire) Client Integration Layer SS c c i iT T e e g g i ic c P P l la a t t f f o o r r m m Tool Integration Layer Data Access Layer Cmd-Line Isentris Chemistry Biology Materials Accord Accord IDBS Oracle ISIS Reporting Statistics ISV Tools Databases Pipeline Pilot - Data Processing and Integration • Integration of data from multiple disparate data sources • Integration of disparate applications – Third party vendors and in- house developed codes under the same environment Pipeline Pilot - Data Processing and Integration • Automated execution of routine processes • Standardised data management • Capture of workflows and deployment of best practice Interoperability
  • Getting Started with Rdock Dr

    Getting Started with Rdock Dr

    Getting Started with rDock Dr. David Morley Getting Started with rDock Dr. David Morley Copyright © 2006 Vernalis Table of Contents Overview ............................................................................................................ vi 1. Prerequisites ..................................................................................................... 1 2. Unpacking the distribution files ............................................................................ 2 3. Building rDock .................................................................................................. 4 4. Running a short validation experiment ................................................................... 6 iv List of Tables 1.1. Required packages for building and running rDock ................................................ 1 2.1. rDock distribution files ..................................................................................... 2 3.1. Standard tmake build targets provided ............................................................... 4 4.1. Complexes included in the cpu_benchmark experiment ...................................... 6 4.2. rDock score components output as SD data fields .................................................. 7 v Overview rDock is a high-throughput molecular docking platform for protein and RNA targets. Under devel- opment at Vernalis (formerly RiboTargets) since 1998, the software (formerly known as RiboDock1), scoring functions, and search protocols have been refined continuously to meet the de-
  • Open Source Molecular Modeling

    Open Source Molecular Modeling

    Accepted Manuscript Title: Open Source Molecular Modeling Author: Somayeh Pirhadi Jocelyn Sunseri David Ryan Koes PII: S1093-3263(16)30118-8 DOI: http://dx.doi.org/doi:10.1016/j.jmgm.2016.07.008 Reference: JMG 6730 To appear in: Journal of Molecular Graphics and Modelling Received date: 4-5-2016 Accepted date: 25-7-2016 Please cite this article as: Somayeh Pirhadi, Jocelyn Sunseri, David Ryan Koes, Open Source Molecular Modeling, <![CDATA[Journal of Molecular Graphics and Modelling]]> (2016), http://dx.doi.org/10.1016/j.jmgm.2016.07.008 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Open Source Molecular Modeling Somayeh Pirhadia, Jocelyn Sunseria, David Ryan Koesa,∗ aDepartment of Computational and Systems Biology, University of Pittsburgh Abstract The success of molecular modeling and computational chemistry efforts are, by definition, de- pendent on quality software applications. Open source software development provides many advantages to users of modeling applications, not the least of which is that the software is free and completely extendable. In this review we categorize, enumerate, and describe available open source software packages for molecular modeling and computational chemistry. 1. Introduction What is Open Source? Free and open source software (FOSS) is software that is both considered \free software," as defined by the Free Software Foundation (http://fsf.org) and \open source," as defined by the Open Source Initiative (http://opensource.org).
  • Protein-Protein Docking and Molecular Dynamics Simulations Elucidated Binding Modes of FUBI-P62 UBA Complex

    Protein-Protein Docking and Molecular Dynamics Simulations Elucidated Binding Modes of FUBI-P62 UBA Complex

    Chongruchiroj et al., 2015 Original Article TJPS The Thai Journal of Pharmaceutical Sciences 39 (4), October - December 2015: 171-179 Protein-Protein Docking and Molecular Dynamics Simulations Elucidated Binding Modes of FUBI-p62 UBA Complex Sumet Chongruchiroj1, Panida Kongsawadworakul 2, Veena Nukoolkarn3, Montree Jaturanpinyo4, 5 6 1,* Wichit Nosoong-noen , Jiraporn Chingunpitak , Jaturong Pratuangdejkul 1Department of Microbiology, Faculty of Pharmacy, Mahidol University, Bangkok 10400, Thailand 2Department of Plant Science, Faculty of Science, Mahidol University, Bangkok 10400, Thailand 3Department of Pharmacognosy, Faculty of Pharmacy, Mahidol University, Bangkok 10400, Thailand 4Department of Manufacturing Pharmacy, Faculty of Pharmacy, Mahidol University, Bangkok 10400, Thailand 5Department of Pharmacy, Faculty of Pharmacy, Mahidol University, Bangkok 10400, Thailand 6School of Pharmacy, Walailak University, Nakhon Si Thammarat 80161, Thailand Abstract The cytosolic Fau protein, a precursor of antimicrobial peptide, composes of ubiquitin-like domain FUBI at N- terminus and ribosomal protein rpS30 at C-terminus. Fau has been important in killing of intracellular Mycobacterium tuberculosis infection through autophagy-targeting p62 mechanism. The p62 adapter protein delivered microbicidal protein rpS30 to autolysosome where it was converted into antimicrobial peptides capable of killing M. tuberculosis in mycobacterial phagosome. Recently, direct interaction of FUBI and p62 UBA domain has been established using immunoprecipitation. In the absence of experimental complex structure of FUBI and p62 UBA, understanding of binding interaction could be extensively characterized using molecular modeling techniques. The aim of this study was to elucidate the binding mode of interaction between FUBI and p62 UBA. Based on the conserved hydrophobic binding regions of FUBI and p62 UBA domain, 334 docked poses were predicted using the ZDOCK and RDOCK protein-protein docking algorithms.
  • Molecular Modeling in Drug Design

    Molecular Modeling in Drug Design

    molecules Molecular Modeling in Drug Design Edited by Rebecca C. Wade and Outi M. H. Salo-Ahen Printed Edition of the Special Issue Published in Molecules www.mdpi.com/journal/molecules Molecular Modeling in Drug Design Molecular Modeling in Drug Design Special Issue Editors Rebecca C. Wade Outi M. H. Salo-Ahen MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade Special Issue Editors Rebecca C. Wade Outi M. H. Salo-Ahen HITS gGmbH/Heidelberg University Abo˚ Akademi University Germany Finland Editorial Office MDPI St. Alban-Anlage 66 4052 Basel, Switzerland This is a reprint of articles from the Special Issue published online in the open access journal Molecules (ISSN 1420-3049) from 2018 to 2019 (available at: https://www.mdpi.com/journal/molecules/ special issues/MMDD) For citation purposes, cite each article independently as indicated on the article page online and as indicated below: LastName, A.A.; LastName, B.B.; LastName, C.C. Article Title. Journal Name Year, Article Number, Page Range. ISBN 978-3-03897-614-1 (Pbk) ISBN 978-3-03897-615-8 (PDF) c 2019 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons license CC BY-NC-ND. Contents About the Special Issue Editors ..................................... vii Preface to ”Molecular Modeling in Drug Design” ..........................
  • Computational Modeling of Rna-Small Molecule and Rna-Protein Interactions

    Computational Modeling of Rna-Small Molecule and Rna-Protein Interactions

    The Texas Medical Center Library DigitalCommons@TMC The University of Texas MD Anderson Cancer Center UTHealth Graduate School of The University of Texas MD Anderson Cancer Biomedical Sciences Dissertations and Theses Center UTHealth Graduate School of (Open Access) Biomedical Sciences 8-2015 COMPUTATIONAL MODELING OF RNA-SMALL MOLECULE AND RNA-PROTEIN INTERACTIONS Lu Chen Follow this and additional works at: https://digitalcommons.library.tmc.edu/utgsbs_dissertations Part of the Bioinformatics Commons, Biophysics Commons, Medicinal-Pharmaceutical Chemistry Commons, Pharmaceutics and Drug Design Commons, Statistical Models Commons, and the Structural Biology Commons Recommended Citation Chen, Lu, "COMPUTATIONAL MODELING OF RNA-SMALL MOLECULE AND RNA-PROTEIN INTERACTIONS" (2015). The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences Dissertations and Theses (Open Access). 626. https://digitalcommons.library.tmc.edu/utgsbs_dissertations/626 This Dissertation (PhD) is brought to you for free and open access by the The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences at DigitalCommons@TMC. It has been accepted for inclusion in The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences Dissertations and Theses (Open Access) by an authorized administrator of DigitalCommons@TMC. For more information, please contact [email protected]. Title page COMPUTATIONAL MODELING OF RNA- SMALL MOLECULE AND RNA-PROTEIN INTERACTIONS A DISSERTATION Presented to the Faculty of The University of Texas Health Science Center at Houston and The University of Texas M.D. Anderson Cancer Center Graduate School of Biomedical Sciences In Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY by Lu Chen, B.S.
  • A Review on Applications of Molecular Docking in Drug Designing

    A Review on Applications of Molecular Docking in Drug Designing

    Indo American Journal of Pharmaceutical Research, 2017 ISSN NO: 2231-6876 A REVIEW ON APPLICATIONS OF MOLECULAR DOCKING IN DRUG DESIGNING 1* 2 1 M. Venkata Saileela , Dr. M. Venkateswar Rao , Venkata Rao Vutla 1Department of Pharmaceutical Analysis, Chalapathi Institute of Pharmaceutical Sciences, Lam, Guntur. 2 Department of Pharmacology, Guntur Medical College, Guntur. ARTICLE INFO ABSTRACT Article history Molecular docking is a computational modelling of structure of complexes formed by two or Received 11/04/2017 more interacting molecules. In the field of molecular modelling, docking is a method which Available online predicts the preferred orientation of one molecule to a second when bound to each other to 30/04/2017 form a stable complex. Knowledge of the preferred orientation in turn may be used to predict the strength of association or binding affinity between two molecules using scoring functions. Keywords Molecular docking is one of the most frequently used in structure based drug design due to its Molecular Modelling, ability to predict the binding-conformation of small molecules ligands to the appropriate Scoring Functions. target binding site. Corresponding author M. Venkata Saileela Department Pharmaceutical Analysis, Chalapathi institute of Pharmaceutical Sciences, Lam, Guntur [email protected] Please cite this article in press as M. Venkata Saileela et al. A Review on Applications of Molecular Docking in Drug Designing. Indo American Journal of Pharmaceutical Research.2017:7(04). C opy right © 2017 This is an Open Access article distributed under the terms of the Indo American journal of Pharmaceutical 8391 Research, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • Intuitive, Reproducible High-Throughput Molecular Dynamics in Galaxy: a Tutorial

    Intuitive, Reproducible High-Throughput Molecular Dynamics in Galaxy: a Tutorial

    bioRxiv preprint doi: https://doi.org/10.1101/2020.05.08.084780; this version posted May 10, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Intuitive, reproducible high-throughput molecular dynamics in Galaxy: a tutorial Simon A. Bray1, Tharindu Senapathi2, Christopher B. Barnett2, , and Björn A. Grüning1, 1Department of Computer Science, University of Freiburg, Georges-Kohler-Allee 106, Freiburg, Germany 2Scientific Computing Research Unit, University of Cape Town, 7700 Cape Town, South Africa This paper is a tutorial developed for the data analysis platform references for further reading. It presumes that the user has Galaxy. The purpose of Galaxy is to make high-throughput a basic understanding of the Galaxy platform. The aim is computational data analysis, such as molecular dynamics, a to guide the user through the various steps of a molecular dy- structured, reproducible and transparent process. In this tu- namics study, from accessing publicly available crystal struc- torial we focus on 3 questions: How are protein-ligand systems tures, to performing MD simulation (leveraging the popular parameterized for molecular dynamics simulation? What kind GROMACS (6, 7) engine), to analysis of the results. of analysis can be carried out on molecular trajectories? How can high-throughput MD be used to study multiple ligands? Af- The entire analysis described in this article can be con- ter finishing you will have learned about force-fields and MD pa- ducted efficiently on any Galaxy server which has the rameterization, how to conduct MD simulation and analysis for needed tools.
  • A Multidisciplinary Approach to Coronavirus Disease (COVID-19)

    A Multidisciplinary Approach to Coronavirus Disease (COVID-19)

    molecules Review A Multidisciplinary Approach to Coronavirus Disease (COVID-19) Aliye Gediz Erturk 1, Arzu Sahin 2, Ebru Bati Ay 3, Emel Pelit 4, Emine Bagdatli 1,*, Irem Kulu 5, Melek Gul 6,*, Seda Mesci 7, Serpil Eryilmaz 8 , Sirin Oba Ilter 9 and Tuba Yildirim 10 1 Department of Chemistry, Faculty of Arts and Sciences, Ordu University, Altınordu, Ordu 52200, Turkey; [email protected] 2 Department of Basic Medical Sciences—Physiology, Faculty of Medicine, U¸sakUniversity, 1-EylulU¸sak64000, Turkey; [email protected] 3 Department of Plant and Animal Production, Suluova Vocational School, Amasya University, Suluova, Amasya 05100, Turkey; [email protected] 4 Department of Chemistry, Faculty of Arts and Sciences, Kırklareli University, Kırklareli 39000, Turkey; [email protected] 5 Department of Chemistry, Faculty of Basic Sciences, Gebze Technical University, Kocaeli 41400, Turkey; [email protected] 6 Department of Chemistry, Faculty of Arts and Sciences, Amasya University, Ipekkoy, Amasya 05100, Turkey 7 Scientific Technical Application and Research Center, Hitit University, Çorum 19030, Turkey; [email protected] 8 Department of Physics, Faculty of Arts and Sciences, Amasya University, Ipekkoy, Amasya 05100, Turkey; [email protected] 9 Food Processing Department, Suluova Vocational School, Amasya University, Suluova, Amasya 05100, Turkey; [email protected] Citation: Gediz Erturk, A.; Sahin, A.; 10 Department of Biology, Faculty of Arts and Sciences, Amasya University, Ipekkoy, Amasya 05100, Turkey; Bati Ay, E.; Pelit, E.; Bagdatli, E.; Kulu, [email protected] I.; Gul, M.; Mesci, S.; Eryilmaz, S.; * Correspondence: [email protected] (E.B.); [email protected] (M.G.); Tel.: +90-358-2421613 (M.G.) Oba Ilter, S.; et al.
  • A Docking Wrapper to Enhance De Novo Molecular Design

    A Docking Wrapper to Enhance De Novo Molecular Design

    DockStream: A Docking Wrapper to Enhance De Novo Molecular Design Jeff Guoa, Jon Paul Janetb, Matthias R. Bauerc, Eva Nittingerd, Kathryn A. Gibline, Kostas Papadopoulosa, Alexey Voronova, Atanas Patronova, Ola Engkvista,f, Christian Margreittera,* a Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden b Medicinal Chemistry, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden c Structure & Biophysics, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK d Medicinal Chemistry, Research and Early Development, Respiratory and Immunology (R&I), BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden e Medicinal Chemistry, Research and Early Development, Oncology R&D, AstraZeneca, Cambridge, UK f Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden * Corresponding author: [email protected] Abstract Recently, we have released the de novo design platform REINVENT in version 2.0. This improved and extended iteration supports far more features and scoring function components, which allows bespoke and tailor-made protocols to maximize impact in small molecule drug discovery projects. A major obstacle of generative models is producing active compounds, in which predictive (QSAR) models have been applied to enrich target activity. However, QSAR models are inherently limited by their applicability domains. To overcome these limitations, we introduce a structure-based scoring component for REINVENT. DockStream is a flexible, stand-alone molecular docking wrapper that provides access to a collection of ligand embedders and docking backends. Using the benchmarking and analysis workflow 2 provided in DockStream, execution and subsequent analysis of a variety of docking configurations can be automated. Docking algorithms vary greatly in performance depending on the target and the benchmarking and analysis workflow provides a streamlined solution to identifying productive docking configurations.